scholarly journals Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6949
Author(s):  
Zhibo Shi ◽  
Guolong Liang ◽  
Longhao Qiu ◽  
Guangming Wan ◽  
Lei Zhao
Keyword(s):  
Compressed Sensing ◽  
Array Signal Processing ◽  
Simultaneous Optimization ◽  
L1 Norm ◽  
Array Design ◽  
Vector Hydrophone ◽  
Grid Algorithm ◽  
The Difference ◽  
Acoustic Environments ◽  
Hydrophone Array

Array design is the primary consideration for array signal processing, and sparse array design is an important and challenging task. In underwater acoustic environments, the vector hydrophone array contains more information than the scalar hydrophone array, but there are few articles focused on the design of the vector hydrophone array. The difference between the vector hydrophone array and the scalar hydrophone array is that each vector hydrophone has three or four channels. When designing a sparse vector hydrophone array, these channels need to be optimized at the same time to ensure the sparsity of the array elements’ number. To solve this problem, this paper introduced the compressed sensing (CS) theory into the vector hydrophone array design, constructed the vector hydrophone array design problem into a globally solvable optimization problem, proposed a CS-based algorithm with the L1 norm suitable for vector hydrophone array, and realized the simultaneous optimization of multiple channels from the same vector hydrophone. At the same time, the off-grid algorithm was added to obtain higher design accuracy. Two design examples verify the effectiveness of the proposed method. The theoretical analysis and simulation results show that compared with the conventional compressed sensing algorithm with the same aperture, the algorithm proposed in this paper used fewer vector hydrophone elements to obtain better fitting of the desired beam pattern.

scholarly journals A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2191
Author(s):  
Huichao Yan ◽  
Ting Chen ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Rong Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spatial Clustering ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cluster Center ◽  
Direction Of Arrival Estimation ◽  
Vector Hydrophone ◽  
Regularization Parameters ◽  
Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

Array design

1968 ◽  
Vol 58 (3) ◽  
pp. 977-991
Author(s):  
Richard A. Haubrich
Keyword(s):  
High Resolution ◽  
Data Processing ◽  
Least Square ◽  
Wave Number ◽  
Array Design ◽  
Least Square Fit ◽  
The Difference ◽  
Set Of Points ◽  
Array Output

abstract Arrays of detectors placed at discrete points are often used in problems requiring high resolution in wave number for a limited number of detectors. The resolution performance of an array depends on the positions of detectors as well as the data processing of the array output. The performance can be expressed in terms of the “spectrum window”. Spectrum windows may be designed by a general least-square fit procedure. An alternate approach is to design the array to obtain the largest uniformly spaced coarray, the set of points which includes all the difference spacings of the array. Some designs obtained from the two methods are given and compared.

scholarly journals Gradient Projection with Approximate L0 Norm Minimization for Sparse Reconstruction in Compressed Sensing

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3373 ◽  
Author(s):  
Ziran Wei ◽  
Jianlin Zhang ◽  
Zhiyong Xu ◽  
Yongmei Huang ◽  
Yong Liu ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Objective Function ◽  
Signal Reconstruction ◽  
Reconstruction Error ◽  
Sparse Signal ◽  
Reconstruction Accuracy ◽  
L1 Norm ◽  
Function Model ◽  
L2 Norm

In the reconstruction of sparse signals in compressed sensing, the reconstruction algorithm is required to reconstruct the sparsest form of signal. In order to minimize the objective function, minimal norm algorithm and greedy pursuit algorithm are most commonly used. The minimum L1 norm algorithm has very high reconstruction accuracy, but this convex optimization algorithm cannot get the sparsest signal like the minimum L0 norm algorithm. However, because the L0 norm method is a non-convex problem, it is difficult to get the global optimal solution and the amount of calculation required is huge. In this paper, a new algorithm is proposed to approximate the smooth L0 norm from the approximate L2 norm. First we set up an approximation function model of the sparse term, then the minimum value of the objective function is solved by the gradient projection, and the weight of the function model of the sparse term in the objective function is adjusted adaptively by the reconstruction error value to reconstruct the sparse signal more accurately. Compared with the pseudo inverse of L2 norm and the L1 norm algorithm, this new algorithm has a lower reconstruction error in one-dimensional sparse signal reconstruction. In simulation experiments of two-dimensional image signal reconstruction, the new algorithm has shorter image reconstruction time and higher image reconstruction accuracy compared with the usually used greedy algorithm and the minimum norm algorithm.

scholarly journals An Improved Squirrel Search Algorithm for Maximum Likelihood DOA Estimation and Application for MEMS Vector Hydrophone Array

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 118343-118358 ◽  
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Xuefang He ◽  
Mingxing Zhang ◽  
Xiuhui Tan
Keyword(s):  
Maximum Likelihood ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Vector Hydrophone ◽  
Hydrophone Array

Off-Grid DOA Estimation Based on Alternating Iterative Weighted Least Squares for Acoustic Vector Hydrophone Array

2020 ◽  
Vol 39 (9) ◽  
pp. 4650-4680 ◽  
Author(s):  
Weidong Wang ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Weijie Tan ◽  
Linlin Mao
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Doa Estimation ◽  
Vector Hydrophone ◽  
Hydrophone Array

Pulsar profile construction based on Double-Redundant-Dictionary and Same-Scale L1-Norm compressed sensing

Optik ◽  
2018 ◽  
Vol 164 ◽  
pp. 617-623 ◽  
Author(s):  
Si-hai You ◽  
Hong-li Wang ◽  
Yi-yang He ◽  
Qiang Xu ◽  
Jing-hui Lu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
L1 Norm ◽  
Redundant Dictionary

scholarly journals Sparse Constrained Least-Squares Reverse Time Migration Based on Kirchhoff Approximation

2021 ◽  
Vol 9 ◽  
Author(s):  
Xu Hong-Qiao ◽  
Wang Xiao-Yi ◽  
Wang Chen-Yuan ◽  
Zhang Jiang-Jie
Keyword(s):  
Least Squares ◽  
Kirchhoff Approximation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
L1 Norm ◽  
Constrained Least Squares ◽  
Geological Structures ◽  
Numerical Examples ◽  
Time Migration ◽  
The Difference

Least-squares reverse time migration (LSRTM) is powerful for imaging complex geological structures. Most researches are based on Born modeling operator with the assumption of small perturbation. However, studies have shown that LSRTM based on Kirchhoff approximation performs better; in particular, it generates a more explicit reflected subsurface and fits large offset data well. Moreover, minimizing the difference between predicted and observed data in a least-squares sense leads to an average solution with relatively low quality. This study applies L1-norm regularization to LSRTM (L1-LSRTM) based on Kirchhoff approximation to compensate for the shortcomings of conventional LSRTM, which obtains a better reflectivity image and gets the residual and resolution in balance. Several numerical examples demonstrate that our method can effectively mitigate the deficiencies of conventional LSRTM and provide a higher resolution image profile.

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